Bielik Anatomy 🧠 Ep.5 - Flash Attention vs Standard Attention | 20x Faster in Triton

Fifth episode of the Bielik Anatomy series. Why does your GPU run out of memory when running large language models? This episode breaks down the math behind Standard Self-Attention, exposes the HBM memory bottleneck, and fixes it by implementing Flash Attention from scratch in OpenAI Triton.

By keeping matrix calculations inside fast SRAM and applying the online softmax algorithm, the standard O(N²) memory trap is bypassed entirely. The result: VRAM usage drops from 7.8 GB down to just 65 MB for an 8k context window, reaching 65 TFLOPS - a 20x speedup over standard PyTorch.

Resources:

• GitHub Repo